11,453 research outputs found
Using Ontologies for the Design of Data Warehouses
Obtaining an implementation of a data warehouse is a complex task that forces
designers to acquire wide knowledge of the domain, thus requiring a high level
of expertise and becoming it a prone-to-fail task. Based on our experience, we
have detected a set of situations we have faced up with in real-world projects
in which we believe that the use of ontologies will improve several aspects of
the design of data warehouses. The aim of this article is to describe several
shortcomings of current data warehouse design approaches and discuss the
benefit of using ontologies to overcome them. This work is a starting point for
discussing the convenience of using ontologies in data warehouse design.Comment: 15 pages, 2 figure
Towards Analytics Aware Ontology Based Access to Static and Streaming Data (Extended Version)
Real-time analytics that requires integration and aggregation of
heterogeneous and distributed streaming and static data is a typical task in
many industrial scenarios such as diagnostics of turbines in Siemens. OBDA
approach has a great potential to facilitate such tasks; however, it has a
number of limitations in dealing with analytics that restrict its use in
important industrial applications. Based on our experience with Siemens, we
argue that in order to overcome those limitations OBDA should be extended and
become analytics, source, and cost aware. In this work we propose such an
extension. In particular, we propose an ontology, mapping, and query language
for OBDA, where aggregate and other analytical functions are first class
citizens. Moreover, we develop query optimisation techniques that allow to
efficiently process analytical tasks over static and streaming data. We
implement our approach in a system and evaluate our system with Siemens turbine
data
Knowledge Guided Integration of Structured and Unstructured Data in Health Decision Process
Data in the health domain is continuously increasing. It is collected from several sources, has several formats and is characterized by its sensibility (protection of personal health data). These characteristics make the management and the expert interaction with the collected data, in order to facilitate decision-making in Health Information Systems (HIS) a challenging field. In this paper, we propose a Knowledge guided integration of structured and unstructured data for health decision process. The knowledge is represented by domain ontology, which allows the integration of structured and unstructured data, stored in NoSQL format. Our motivation is to combine the confirmed advantages of ontologies and NoSQL databases both in data integration and decision aided processes. The proposed ontology has been implemented and evaluated using quality metrics. The approach was evaluated and results show response time optimization, compared with traditional approaches, and improvement of data relevance
MDDQL: an ontology driven, multi-lingual query language and system for an integrated view of heterogeneous data sources
Query languages and keywords based search engines are
conventionally specified and implemented with the
emphasis put on syntactic rules to which query typing and
answering must be bound. MDDQL is a query language
and system that operates on a semantic model in terms of a
graph based ontology. As a software technology, MDDQL
allows the meaning of/and associations between
information to be known and processed at execution time at
following levels: (a) driving the user to the construction of,
as meaningful as possible, queries with an advanced
concept-based search method, (b) resolving high level
queries into various data source specific query statements.
In addition, queries can be posed in more than one natural
sub-language. The major goal behind this approach has
been the simplification and scalability of both tasks: query
construction, even within multi-lingual user communities,
and addressing of a large number of possibly semantically
heterogeneous data sources in a distributed environment
Framework for automatic generation of ontology mappings
Some of the most outstanding problems in Computer Science (e.g. access to heterogeneous information sources, use of different e-commerce standards, ontology translation, etc.) are often approached through the identification of ontology mappings. A manual mapping generation slows down, or even makes unfeasible, the solution of particular cases of the aforementioned problems via ontology mappings. Some algorithms and formal models for partial tasks of automatic generation of mappings have been proposed. However, an integrated framework to solve this problem is still missing. In this paper, we present a framework for automatic ontology mapping generation, and a partial implementation of it. Our proposal is that this integrated vision can guide, not only our future work, but also the future work of other researchers. In the implementation carried out, we have built a mapping ontology with knowledge on ontology mappings
An Extended Semantic Interoperability Model for Distributed Electronic Health Record Based on Fuzzy Ontology Semantics
Semantic interoperability of distributed electronic health record (EHR) systems is a crucial problem for querying EHR and machine learning projects. The main contribution of this paper is to propose and implement a fuzzy ontology-based semantic interoperability framework for distributed EHR systems. First, a separate standard ontology is created for each input source. Second, a unified ontology is created that merges the previously created ontologies. However, this crisp ontology is not able to answer vague or uncertain queries. We thirdly extend the integrated crisp ontology into a fuzzy ontology by using a standard methodology and fuzzy logic to handle this limitation. The used dataset includes identified data of 100 patients. The resulting fuzzy ontology includes 27 class, 58 properties, 43 fuzzy data types, 451 instances, 8376 axioms, 5232 logical axioms, 1216 declarative axioms, 113 annotation axioms, and 3204 data property assertions. The resulting ontology is tested using real data from the MIMIC-III intensive care unit dataset and real archetypes from openEHR. This fuzzy ontology-based system helps physicians accurately query any required data about patients from distributed locations using near-natural language queries. Domain specialists validated the accuracy and correctness of the obtained resultsThis work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (NRF-2021R1A2B5B02002599)S
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